1. Field of the Invention
This invention relates to the visual inspection of electronic component leads as a step in a total placement system. More particularly, it relates to determining presence/absence, position and orientation of fine pitch leads on a surface mount component such as a TAB device located on a robotic end effector.
2. Description of the Prior Art
For high speed throughput in a automated component placement system great accuracy is required. First, the presence/absence, position and orientation of component leads must be determined. It is conventional to use computer vision techniques to aid in this determination.
The prior art has illustrated in IBM Technical Disclosure Bulletin Volume 30, Number 1, 1987, pp. 228 "Surface Mounted Device Placement" discloses a technique for inspecting a component and its leads prior to placement with a high degree of accuracy. However, it differs from the present invention in that it does not provide for the accuracy required for fine pitch components.
The principle differences resulting in less accuracy are that this reference uses only one camera and determines lead position on the basis of binary pixels rather than the additional information contained in gray level pixels.
Similarly. IBM Technical Disclosure Bulletin Volume 31, Number 10, March 1989, pp. 222 "Assembly Technique Replacing the Electronic Components on Printed Circuit Wiring Patterns" discloses the use of computer vision processing, without detail, for inspecting component lead presence, condition and orientation as a step in a total placement system.
IBM Technical Disclosure Bulletin Vol. 31, No. 9, 2/89, p. 186, Robotic Scanning Laser Placement, Solder & Desolder Devices discloses the use of a CCD camera and a vision and system to determine X, Y and theta offsets. Conventional CCD video cameras used in machine vision systems typically have resolution of 492 (vertical) by 512 (horizontal) pixels. Working with such a large pixel array requires time and computational resources in large amounts. It is desirable to optimize time and resource usage without sacrificing accuracy in vision processors.